From 4ff74ec0bc829b2cf11d6dc8d9b787085e5f1c0e Mon Sep 17 00:00:00 2001 From: Lukas Stelzl Date: Tue, 5 Mar 2019 11:15:57 +0100 Subject: [PATCH 1/3] adding Ala5 optimization from Koefinger et al., 2019 --- examples/ala5_optimize/ala5-bioen.ipynb | 3264 ++++++++++++++++++ examples/ala5_optimize/ala5-release-1.tar.gz | Bin 0 -> 34618146 bytes examples/ala5_optimize/lbfgs_2.yaml | 55 + examples/ala5_optimize/thetas2.dat | 80 + 4 files changed, 3399 insertions(+) create mode 100644 examples/ala5_optimize/ala5-bioen.ipynb create mode 100644 examples/ala5_optimize/ala5-release-1.tar.gz create mode 100644 examples/ala5_optimize/lbfgs_2.yaml create mode 100644 examples/ala5_optimize/thetas2.dat diff --git a/examples/ala5_optimize/ala5-bioen.ipynb b/examples/ala5_optimize/ala5-bioen.ipynb new file mode 100644 index 0000000..d2f630a --- /dev/null +++ b/examples/ala5_optimize/ala5-bioen.ipynb @@ -0,0 +1,3264 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "toc": true + }, + "source": [ + "

Table of Contents

\n", + "
" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Ala5 BioEn-optimize API example" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Some code to generate the BioEn inputs to use BioEn optimize via its Python API." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "import os\n", + "import pandas as pd\n", + "\n", + "def loop_data_files_ala5(jkeys, sim_fn, r_tup=(2,6)):\n", + " sim_fn_l = []\n", + " c = 0\n", + " for i, k in enumerate(jkeys):\n", + " for r in range(r_tup[0], r_tup[1]): \n", + " fn = sim_fn.format(r,k)\n", + " if os.path.exists(fn):\n", + " c += 1\n", + " sim_fn_l.append(fn)\n", + " return c, sim_fn_l\n", + "\n", + "\n", + "def ala5_jcouplings_bioen_input(jkeys, J,\n", + " sim_fn=\"../../jcoupling_sk10/dft2/r{}_J{}_dft2_deg_err_bl5\", skip=1,\n", + " return_exp_details=False, err_column_name=\"sigma\"):\n", + " \n", + " # definition of the output arrays\n", + " n_exp, sim_fn_l = loop_data_files_ala5(jkeys, sim_fn)\n", + " n_stru = len(np.genfromtxt(sim_fn_l[0])[::skip])\n", + " \n", + " y_tilde = np.zeros((n_exp, n_stru))\n", + " yTilde = np.zeros((n_exp, n_stru))\n", + " Y_Tilde = np.zeros(n_exp)\n", + " \n", + " # experiment unscale and experimental error\n", + " exp_unscaled = np.zeros(n_exp)\n", + " exp_sigma = np.zeros(n_exp)\n", + " \n", + " res_jcp_l = []\n", + " c = 0\n", + " for i, k in enumerate(jkeys):\n", + " for r in range(2,6): \n", + " #fn = \"../../orig/r{}_J{}_orig_deg_err_bl10\".format(r,k)\n", + " fn = sim_fn.format(r,k)\n", + " if os.path.exists(fn):\n", + " sim_ar = np.genfromtxt(fn)[::skip]\n", + " y_tilde[c,:] = sim_ar\n", + " print c, r, k\n", + " exp = J[(J['Jtype']==k) & (J['res']==r)]\n", + " \n", + " # error \n", + " obsv_err = exp[err_column_name].values\n", + " yTilde[c,:] = np.copy(sim_ar) / obsv_err [:,None]\n", + " Y_Tilde[c] = exp.Jexpt.values / obsv_err \n", + " \n", + " exp_unscaled[c] = exp.Jexpt.values\n", + " exp_sigma[c] = obsv_err \n", + " \n", + " res_jcp_l.append(\"r{} {}\".format(r,k)) \n", + " c +=1\n", + " else:\n", + " print \"missing\"\n", + " \n", + " if return_exp_details:\n", + " return res_jcp_l, y_tilde, yTilde, Y_Tilde.T, np.column_stack((exp_unscaled, \n", + " exp_sigma))\n", + " else:\n", + " return res_jcp_l, y_tilde, yTilde, Y_Tilde.T" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Read $\\phi$ and $\\psi$ dihedral angles" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "dft2.tar.bz2\n", + "phi_psi_deg.tar.gz\n", + "ala_phi_psi.txt\n" + ] + } + ], + "source": [ + "! tar xfv ala5-release-1.tar.gz" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "c_phi_psi_deg_4\n", + "c_phi_psi_deg_3\n", + "c_phi_psi_deg_2\n", + "c_phi_psi_deg_6\n", + "c_phi_psi_deg_5\n" + ] + } + ], + "source": [ + "! tar xfv phi_psi_deg.tar.gz" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### example calculation for J-couplings\n", + "\n", + "c.f., calculation as in Best and co-workers 2008." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "def karplus_eq(theta, A, B, C, delta):\n", + " return A*np.cos(theta+delta)**2 + B*np.cos(theta+delta) + C\n", + "\n", + "def calc_j3_hn_ha(phi, A=9.14, B=-2.28, C=-0.29, delta=np.deg2rad(-64.51)):\n", + " return karplus_eq(phi, A, B, C, delta)" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "deg_l = []\n", + "for i in range(2,7):\n", + " deg_l.append(np.loadtxt(\"./c_phi_psi_deg_{}\".format(i)))" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [], + "source": [ + "j3_hnha = calc_j3_hn_ha(np.deg2rad(deg_l[1])[:,0], delta=np.deg2rad(-64.51))" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(100001,)" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "j3_hnha.shape" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "dft2/\n", + "dft2/r4_J3JHNC_dft2_deg_err_bl5\n", + "dft2/r5_J1JNCA_dft2_deg_err_bl5\n", + "dft2/r3_J3JHAC_dft2_deg_err_bl5\n", + "dft2/r2_J3JHNCA_dft2_deg_err_bl5\n", + "dft2/r4_J1JNCA_dft2_deg_err_bl5\n", + "dft2/r5_J2JNCA_dft2_deg_err_bl5\n", + "dft2/r4_J3JHNCB_dft2_deg_err_bl5\n", + "dft2/r5_J3JHNHA_dft2_deg_err_bl5\n", + "dft2/r2_J3JHNC_dft2_deg_err_bl5\n", + "dft2/r2_J3JHAC_dft2_deg_err_bl5\n", + "dft2/r3_J3JHNCB_dft2_deg_err_bl5\n", + "dft2/r4_J2JNCA_dft2_deg_err_bl5\n", + "dft2/r5_J3JHNCA_dft2_deg_err_bl5\n", + "dft2/r2_J3JHNHA_dft2_deg_err_bl5\n", + "dft2/r2_J1JNCA_dft2_deg_err_bl5\n", + "dft2/r5_J3JHNCB_dft2_deg_err_bl5\n", + "dft2/r3_J1JNCA_dft2_deg_err_bl5\n", + "dft2/r3_J3JHNCA_dft2_deg_err_bl5\n", + "dft2/r4_J3JHAC_dft2_deg_err_bl5\n", + "dft2/r2_J3JCC_dft2_deg_err_bl5\n", + "dft2/r5_J3JHAC_dft2_deg_err_bl5\n", + "dft2/r3_J3JHNHA_dft2_deg_err_bl5\n", + "dft2/r4_J3JHNHA_dft2_deg_err_bl5\n", + "dft2/r2_J3JHNCB_dft2_deg_err_bl5\n", + "dft2/r4_J3JHNCA_dft2_deg_err_bl5\n", + "dft2/r5_J3JHNC_dft2_deg_err_bl5\n", + "dft2/r3_J2JNCA_dft2_deg_err_bl5\n", + "dft2/dft2_deg_err_bl5_j.dat\n", + "dft2/r2_J2JNCA_dft2_deg_err_bl5\n" + ] + } + ], + "source": [ + "! tar xfv dft2.tar.bz2" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [], + "source": [ + "ref_j3_hnha = np.genfromtxt(\"dft2/r2_J3JHNHA_dft2_deg_err_bl5\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Zoom in onto initial part of trajectory." + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Text(0.5,0,'Time [ps]')" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig, ax = plt.subplots(figsize=(15,3))\n", + "plt.plot(ref_j3_hnha, \"o\", alpha=0.4)\n", + "plt.plot(j3_hnha, \"-\", alpha=0.4)\n", + "plt.xlim(0,1000)\n", + "plt.ylabel(\"$^3J_{HNHA}$ [Hz]\")\n", + "plt.xlabel(\"Time [ps]\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Note that diverse J-couplings are calculated for the trajectory." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Read J-couplings" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [], + "source": [ + "jkeys = [\"3JHNHA\",\"3JHNC\",\"3JHAC\",\"3JCC\",\"3JHNCB\",\"1JNCA\",\"2JNCA\",\"3JHNCA\"]" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(28, 50001)" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "skip = 20 # 20-> 50k\n", + "j_ar = np.zeros((28, 1000001))[:,::skip]\n", + "j_ar.shape" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "dft2_deg_err_bl5_j.dat\t r4_J1JNCA_dft2_deg_err_bl5\r\n", + "r2_J1JNCA_dft2_deg_err_bl5 r4_J2JNCA_dft2_deg_err_bl5\r\n", + "r2_J2JNCA_dft2_deg_err_bl5 r4_J3JHAC_dft2_deg_err_bl5\r\n", + "r2_J3JCC_dft2_deg_err_bl5 r4_J3JHNCA_dft2_deg_err_bl5\r\n", + "r2_J3JHAC_dft2_deg_err_bl5 r4_J3JHNCB_dft2_deg_err_bl5\r\n", + "r2_J3JHNCA_dft2_deg_err_bl5 r4_J3JHNC_dft2_deg_err_bl5\r\n", + "r2_J3JHNCB_dft2_deg_err_bl5 r4_J3JHNHA_dft2_deg_err_bl5\r\n", + "r2_J3JHNC_dft2_deg_err_bl5 r5_J1JNCA_dft2_deg_err_bl5\r\n", + "r2_J3JHNHA_dft2_deg_err_bl5 r5_J2JNCA_dft2_deg_err_bl5\r\n", + "r3_J1JNCA_dft2_deg_err_bl5 r5_J3JHAC_dft2_deg_err_bl5\r\n", + "r3_J2JNCA_dft2_deg_err_bl5 r5_J3JHNCA_dft2_deg_err_bl5\r\n", + "r3_J3JHAC_dft2_deg_err_bl5 r5_J3JHNCB_dft2_deg_err_bl5\r\n", + "r3_J3JHNCA_dft2_deg_err_bl5 r5_J3JHNC_dft2_deg_err_bl5\r\n", + "r3_J3JHNCB_dft2_deg_err_bl5 r5_J3JHNHA_dft2_deg_err_bl5\r\n", + "r3_J3JHNHA_dft2_deg_err_bl5\r\n" + ] + } + ], + "source": [ + "! ls dft2" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [], + "source": [ + "J_dft2 = pd.read_table(\"dft2/dft2_deg_err_bl5_j.dat\", \n", + " delim_whitespace=True, skipfooter=1, engine='python')" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "dft2\n", + "0 2 3JHNHA\n", + "1 3 3JHNHA\n", + "2 4 3JHNHA\n", + "3 5 3JHNHA\n", + "4 2 3JHNC\n", + "missing\n", + "5 4 3JHNC\n", + "6 5 3JHNC\n", + "7 2 3JHAC\n", + "8 3 3JHAC\n", + "9 4 3JHAC\n", + "10 5 3JHAC\n", + "11 2 3JCC\n", + "missing\n", + "missing\n", + "missing\n", + "12 2 3JHNCB\n", + "13 3 3JHNCB\n", + "14 4 3JHNCB\n", + "15 5 3JHNCB\n", + "16 2 1JNCA\n", + "17 3 1JNCA\n", + "18 4 1JNCA\n", + "19 5 1JNCA\n", + "20 2 2JNCA\n", + "21 3 2JNCA\n", + "22 4 2JNCA\n", + "23 5 2JNCA\n", + "24 2 3JHNCA\n", + "25 3 3JHNCA\n", + "26 4 3JHNCA\n", + "27 5 3JHNCA\n" + ] + } + ], + "source": [ + "#tabel_l = [J_dft1, J_dft2, J_orig]\n", + "#set_names_l = [\"dft1\", \"dft2\", \"orig\"]\n", + "\n", + "tabel_l = [ J_dft2]\n", + "set_names_l = [ \"dft2\"]\n", + "\n", + "#dft1_fn = \"/r{}_J{}_dft1_deg_err_bl5\"\n", + "dft2_fn = \"/r{}_J{}_dft2_deg_err_bl5\"\n", + "#orig_fn = \"/r{}_J{}_orig_deg_err_bl5\"\n", + "\n", + "fn_l = [dft2_fn]\n", + "\n", + "raw_l = []\n", + "j_path=\"./\"\n", + "\n", + "for set_i, karplus_set in enumerate(set_names_l):\n", + " \n", + " print karplus_set\n", + " \n", + " sim_fn = j_path + karplus_set \n", + " #! ls $sim_fn\n", + " sim_fn += fn_l[set_i]\n", + " \n", + " o = ala5_jcouplings_bioen_input(jkeys, tabel_l[set_i], skip=2,\n", + " return_exp_details=True,\n", + " err_column_name=\"sigma\",\n", + " sim_fn=sim_fn)\n", + " \n", + " raw_l.append(o)" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [], + "source": [ + "for set_i, karplus_set in enumerate(set_names_l):\n", + " \n", + " \n", + " res_jcp_l, y_tilde, yTilde, Y_Tilde, exp_obs_err = raw_l[set_i]\n", + " \n", + " N = y_tilde.shape[1]\n", + " w0 = (np.matrix(np.ones(N)/(N))).T\n", + " \n", + " opt_dir = karplus_set+\"_for_opt\"\n", + " #! mkdir -p $karplus_set\n", + " ! mkdir -p $opt_dir\n", + " #np.savetxt(\"for_opt/G.txt\", G)\n", + " np.savetxt(opt_dir+\"/w0.txt\", w0)\n", + " np.savetxt(opt_dir+\"/y.txt\", y_tilde)\n", + " np.savetxt(opt_dir+\"/yTilde.txt\", yTilde)\n", + " np.savetxt(opt_dir+\"/YTilde.txt\", Y_Tilde)" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "dft2_deg_err_bl5_j.dat\t r4_J1JNCA_dft2_deg_err_bl5\r\n", + "r2_J1JNCA_dft2_deg_err_bl5 r4_J2JNCA_dft2_deg_err_bl5\r\n", + "r2_J2JNCA_dft2_deg_err_bl5 r4_J3JHAC_dft2_deg_err_bl5\r\n", + "r2_J3JCC_dft2_deg_err_bl5 r4_J3JHNCA_dft2_deg_err_bl5\r\n", + "r2_J3JHAC_dft2_deg_err_bl5 r4_J3JHNCB_dft2_deg_err_bl5\r\n", + "r2_J3JHNCA_dft2_deg_err_bl5 r4_J3JHNC_dft2_deg_err_bl5\r\n", + "r2_J3JHNCB_dft2_deg_err_bl5 r4_J3JHNHA_dft2_deg_err_bl5\r\n", + "r2_J3JHNC_dft2_deg_err_bl5 r5_J1JNCA_dft2_deg_err_bl5\r\n", + "r2_J3JHNHA_dft2_deg_err_bl5 r5_J2JNCA_dft2_deg_err_bl5\r\n", + "r3_J1JNCA_dft2_deg_err_bl5 r5_J3JHAC_dft2_deg_err_bl5\r\n", + "r3_J2JNCA_dft2_deg_err_bl5 r5_J3JHNCA_dft2_deg_err_bl5\r\n", + "r3_J3JHAC_dft2_deg_err_bl5 r5_J3JHNCB_dft2_deg_err_bl5\r\n", + "r3_J3JHNCA_dft2_deg_err_bl5 r5_J3JHNC_dft2_deg_err_bl5\r\n", + "r3_J3JHNCB_dft2_deg_err_bl5 r5_J3JHNHA_dft2_deg_err_bl5\r\n", + "r3_J3JHNHA_dft2_deg_err_bl5\r\n" + ] + } + ], + "source": [ + "! ls dft2" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Prepare BioEn optimization" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Function to run series of BioEn optimizations" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [], + "source": [ + "from collections import defaultdict\n", + "from bioen.analyze import utils" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [], + "source": [ + "def run_theta_series(w0, y, yTilde, Y_Tilde, opt_method=\"forces\", verbose=True,\n", + " theta_l=[100000, 10000, 1000, 200, 10, 1, 0.2, 0.1, 0],\n", + " cfg=None):\n", + " wopt_d = {}\n", + " yopt_d = {}\n", + " time_d = {}\n", + " S_d = {}\n", + " chi2_d = {}\n", + " L_d = {}\n", + " \n", + " N = len(w0.flat)\n", + " M = yTilde.shape[0]\n", + " \n", + " G = optimize.log_weights.getGs(w0)\n", + " GInit = optimize.log_weights.getGs(w0)\n", + " \n", + " forces_init = optimize.forces.init_forces(M)\n", + " \n", + " if np.all(cfg) is None:\n", + " # using defaults\n", + " cfg = optimize.minimize.Parameters()\n", + " \n", + " for i, theta in enumerate(theta_l):\n", + " if verbose:\n", + " print theta\n", + " \n", + " if opt_method == \"log-weights\":\n", + " start=time.time()\n", + " \n", + " \n", + " \n", + " o = optimize.log_weights.find_optimum(GInit, G, \n", + " y, yTilde, Y_Tilde, theta, cfg)\n", + " end=time.time()\n", + " dt=end-start\n", + " wopt, yopt, gopt, f_initial, f_final = o \n", + " \n", + " # calculate S and chi2\n", + " S = utils.get_entropy(w0, wopt)*-1 # to get KL \n", + " chi2 = optimize.common.chiSqrTerm(wopt, yTilde, Y_Tilde)\n", + " \n", + " \n", + " Ginit = optimize.log_weights.getGs(wopt)\n", + " \n", + " if opt_method == 'forces':\n", + " start=time.time()\n", + "\n", + " o = optimize.forces.find_optimum(forces_init, w0,\n", + " np.matrix(y), np.matrix(yTilde),\n", + " np.matrix(Y_Tilde), theta, cfg)\n", + " end=time.time()\n", + " dt=end-start\n", + "\n", + " # outputs from minimization\n", + " wopt, yopt, forces_opt, f_initial, f_final, chi2, S = o\n", + " \n", + " # Get forces for next optimization\n", + " forces_init = forces_opt.reshape((M,1))\n", + " \n", + " \n", + " if verbose:\n", + " # Needs fixing here\n", + " if i > 0:\n", + " print(\"start F {}, final F {}\".format(L_d[theta_l[i-1]], f_final))\n", + " #print(\"entropy {}, chi2 {}, F {}\".format(S, chi2, chi2-theta*S))\n", + " #print(\"Time {}\".format(dt))\n", + " \n", + " wopt_d[theta] = wopt\n", + " yopt_d[theta] = yopt\n", + " time_d[theta] = dt\n", + " S_d[theta] = S\n", + " chi2_d[theta] = chi2 \n", + " L_d[theta] = f_final\n", + " \n", + " return wopt_d, yopt_d, L_d, chi2_d, S_d" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Handling output" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [], + "source": [ + "def overview_table(theta_l, L_d, chi2_d, S_d, out_path=\"out\"):\n", + " data = []\n", + " for i, theta in enumerate(theta_l):\n", + " data.append([theta, S_d[theta], chi2_d[theta], L_d[theta]])\n", + " np.savetxt(out_path+\"/opt_data.dat\", data, header=\"theta, S, chiSqr, F, offset\") \n", + " \n", + " \n", + "def bioen_save_out_array(ar_theta_dict, output_name=\"theta{}_dat.txt\"):\n", + " for key, value in ar_theta_dict.items():\n", + " np.savetxt(output_name.format(key), value) \n", + " " + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [], + "source": [ + "from bioen import optimize\n", + "import os, sys, time" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": {}, + "outputs": [], + "source": [ + "! cp ~/bio/BioEn/ala5/ala5_ph2/ala5_ph2_2/nb_dft1/run1/thetas2.dat . \n", + "! cp /home/tb/lustelzl/DATA/Projects/BioEn/rna-example/lbfgs_2.yaml . " + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": {}, + "outputs": [], + "source": [ + "cfg = optimize.util.load_template_config_yaml(\n", + " \"lbfgs_2.yaml\", \"lbfgs\")\n", + "\n", + "#cfg = optimize.minimize.Parameters('gsl')\n", + "cfg[\"verbose\"] = True" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": {}, + "outputs": [], + "source": [ + "theta_ar = np.genfromtxt(\"thetas2.dat\")" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "dft2\n", + "dft2_for_opt/\n", + "out/log-weights/dft2/for_opt\n", + "100000.0\n", + "fmin_initial 26.6679801306\n", + "------------------------\n", + "LOGW -- Library L-BFGS/C\n", + "------------------------\n", + "time elapsed 0.552150964737\n", + "========================\n", + "fmin_initial = 26.6679801306\n", + "fmin_final = 26.6617265017\n", + "========================\n", + "83955.7862\n", + "fmin_initial 26.6679801306\n", + "------------------------\n", + "LOGW -- Library L-BFGS/C\n", + "------------------------\n", + "time elapsed 0.504796028137\n", + "========================\n", + "fmin_initial = 26.6679801306\n", + "fmin_final = 26.6605320848\n", + "========================\n", + "start F 26.6617265017, final F 26.6605320848\n", + "70485.7403645\n", + "fmin_initial 26.6679801306\n", + "------------------------\n", + "LOGW -- Library L-BFGS/C\n", + "------------------------\n", + "time elapsed 0.0361850261688\n", + "========================\n", + "fmin_initial = 26.6679801306\n", + "fmin_final = 26.6591096937\n", + "========================\n", + "start F 26.6605320848, final F 26.6591096937\n", + "59176.8574819\n", + "fmin_initial 26.6679801306\n", + "------------------------\n", + "LOGW -- Library L-BFGS/C\n", + "------------------------\n", + "time elapsed 0.0512659549713\n", + "========================\n", + "fmin_initial = 26.6679801306\n", + "fmin_final = 26.6574158767\n", + "========================\n", + "start F 26.6591096937, final F 26.6574158767\n", + "49682.3959473\n", + "fmin_initial 26.6679801306\n", + "------------------------\n", + "LOGW -- Library L-BFGS/C\n", + "------------------------\n", + "time elapsed 0.0817141532898\n", + "========================\n", + "fmin_initial = 26.6679801306\n", + "fmin_final = 26.6553989312\n", + "========================\n", + "start F 26.6574158767, final F 26.6553989312\n", + "41711.2461206\n", + "fmin_initial 26.6679801306\n", + "------------------------\n", + "LOGW -- Library L-BFGS/C\n", + "------------------------\n", + "time elapsed 0.0524799823761\n", + "========================\n", + "fmin_initial = 26.6679801306\n", + "fmin_final = 26.6529973417\n", + "========================\n", + "start F 26.6553989312, final F 26.6529973417\n", + "35019.0046143\n", + "fmin_initial 26.6679801306\n", + "------------------------\n", + "LOGW -- Library L-BFGS/C\n", + "------------------------\n", + "time elapsed 0.0312569141388\n", + "========================\n", + "fmin_initial = 26.6679801306\n", + "fmin_final = 26.6501379381\n", + "========================\n", + "start F 26.6529973417, final F 26.6501379381\n", + "29400.4806433\n", + "fmin_initial 26.6679801306\n", + "------------------------\n", + "LOGW -- Library L-BFGS/C\n", + "------------------------\n", + "time elapsed 0.051726102829\n", + "========================\n", + "fmin_initial = 26.6679801306\n", + "fmin_final = 26.6467337031\n", + "========================\n", + "start F 26.6501379381, final F 26.6467337031\n", + "24683.4046707\n", + "fmin_initial 26.6679801306\n", + "------------------------\n", + "LOGW -- Library L-BFGS/C\n", + "------------------------\n", + "time elapsed 0.0693180561066\n", + "========================\n", + "fmin_initial = 26.6679801306\n", + "fmin_final = 26.6426811916\n", + "========================\n", + "start F 26.6467337031, final F 26.6426811916\n", + "20723.1464522\n", + "fmin_initial 26.6679801306\n", + "------------------------\n", + "LOGW -- Library L-BFGS/C\n", + "------------------------\n", + "time elapsed 0.0585899353027\n", + "========================\n", + "fmin_initial = 26.6679801306\n", + "fmin_final = 26.6378574684\n", + "========================\n", + "start F 26.6426811916, final F 26.6378574684\n", + "17398.2805293\n", + "fmin_initial 26.6679801306\n", + "------------------------\n", + "LOGW -- Library L-BFGS/C\n", + "------------------------\n", + "time elapsed 0.0786001682281\n", + "========================\n", + "fmin_initial = 26.6679801306\n", + "fmin_final = 26.6321165005\n", + "========================\n", + "start F 26.6378574684, final F 26.6321165005\n", + "14606.8632036\n", + "fmin_initial 26.6679801306\n", + "------------------------\n", + "LOGW -- Library L-BFGS/C\n", + "------------------------\n", + "time elapsed 0.0389318466187\n", + "========================\n", + "fmin_initial = 26.6679801306\n", + "fmin_final = 26.6252849103\n", + "========================\n", + "start F 26.6321165005, final F 26.6252849103\n", + "12263.3068418\n", + "fmin_initial 26.6679801306\n", + "------------------------\n", + "LOGW -- Library L-BFGS/C\n", + "------------------------\n", + "time elapsed 0.0296850204468\n", + "========================\n", + "fmin_initial = 26.6679801306\n", + "fmin_final = 26.6171569785\n", + "========================\n", + "start F 26.6252849103, final F 26.6171569785\n", + "10295.7556731\n", + "fmin_initial 26.6679801306\n", + "------------------------\n", + "LOGW -- Library L-BFGS/C\n", + "------------------------\n", + "time elapsed 0.0496380329132\n", + "========================\n", + "fmin_initial = 26.6679801306\n", + "fmin_final = 26.6074887932\n", + "========================\n", + "start F 26.6171569785, final F 26.6074887932\n", + "8643.8826206\n", + "fmin_initial 26.6679801306\n", + "------------------------\n", + "LOGW -- Library L-BFGS/C\n", + "------------------------\n", + "time elapsed 0.050507068634\n", + "========================\n", + "fmin_initial = 26.6679801306\n", + "fmin_final = 26.5959914117\n", + "========================\n", + "start F 26.6074887932, final F 26.5959914117\n", + "7257.03961232\n", + "fmin_initial 26.6679801306\n", + "------------------------\n", + "LOGW -- Library L-BFGS/C\n", + "------------------------\n", + "time elapsed 0.0357158184052\n", + "========================\n", + "fmin_initial = 26.6679801306\n", + "fmin_final = 26.5823229035\n", + "========================\n", + "start F 26.5959914117, final F 26.5823229035\n", + "6092.70466137\n", + "fmin_initial 26.6679801306\n", + "------------------------\n", + "LOGW -- Library L-BFGS/C\n", + "------------------------\n", + "time elapsed 0.0255439281464\n", + "========================\n", + "fmin_initial = 26.6679801306\n", + "fmin_final = 26.5660791374\n", + "========================\n", + "start F 26.5823229035, final F 26.5660791374\n", + "5115.17809929\n", + "fmin_initial 26.6679801306\n", + "------------------------\n", + "LOGW -- Library L-BFGS/C\n", + "------------------------\n", + "time elapsed 0.0701360702515\n", + "========================\n", + "fmin_initial = 26.6679801306\n", + "fmin_final = 26.5467831729\n", + "========================\n", + "start F 26.5660791374, final F 26.5467831729\n", + "4294.48798879\n", + "fmin_initial 26.6679801306\n", + "------------------------\n", + "LOGW -- Library L-BFGS/C\n", + "------------------------\n", + "time elapsed 0.0494990348816\n", + "========================\n", + "fmin_initial = 26.6679801306\n", + "fmin_final = 26.5238731369\n", + "========================\n", + "start F 26.5467831729, final F 26.5238731369\n", + "3605.47115425\n", + "fmin_initial 26.6679801306\n", + "------------------------\n", + "LOGW -- Library L-BFGS/C\n", + "------------------------\n", + "time elapsed 0.0411350727081\n", + "========================\n", + "fmin_initial = 26.6679801306\n", + "fmin_final = 26.4966884908\n", + "========================\n", + "start F 26.5238731369, final F 26.4966884908\n", + "3027.00165376\n", + "fmin_initial 26.6679801306\n", + "------------------------\n", + "LOGW -- Library L-BFGS/C\n", + "------------------------\n", + "time elapsed 0.035854101181\n", + "========================\n", + "fmin_initial = 26.6679801306\n", + "fmin_final = 26.4644546422\n", + "========================\n", + "start F 26.4966884908, final F 26.4644546422\n", + "2541.3430367\n", + "fmin_initial 26.6679801306\n", + "------------------------\n", + "LOGW -- Library L-BFGS/C\n", + "------------------------\n", + "time elapsed 0.0609550476074\n", + "========================\n", + "fmin_initial = 26.6679801306\n", + "fmin_final = 26.4262659923\n", + "========================\n", + "start F 26.4644546422, final F 26.4262659923\n", + "2133.6045265\n", + "fmin_initial 26.6679801306\n", + "------------------------\n", + "LOGW -- Library L-BFGS/C\n", + 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"------------------------\n", + "LOGW -- Library L-BFGS/C\n", + "------------------------\n", + "time elapsed 0.0611710548401\n", + "========================\n", + "fmin_initial = 26.6679801306\n", + "fmin_final = 25.5765252876\n", + "========================\n", + "start F 25.739666204, final F 25.5765252876\n", + "442.149990737\n", + "fmin_initial 26.6679801306\n", + "------------------------\n", + "LOGW -- Library L-BFGS/C\n", + "------------------------\n", + "time elapsed 0.0396249294281\n", + "========================\n", + "fmin_initial = 26.6679801306\n", + "fmin_final = 25.3875348602\n", + "========================\n", + "start F 25.5765252876, final F 25.3875348602\n", + "371.210500907\n", + "fmin_initial 26.6679801306\n", + "------------------------\n", + "LOGW -- Library L-BFGS/C\n", + "------------------------\n", + "time elapsed 0.05326795578\n", + "========================\n", + "fmin_initial = 26.6679801306\n", + "fmin_final = 25.1696007703\n", + 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0.0559840202332\n", + "========================\n", + "fmin_initial = 26.6679801306\n", + "fmin_final = 24.3114060946\n", + "========================\n", + "start F 24.6344615489, final F 24.3114060946\n", + "184.426270859\n", + "fmin_initial 26.6679801306\n", + "------------------------\n", + "LOGW -- Library L-BFGS/C\n", + "------------------------\n", + "time elapsed 0.0575439929962\n", + "========================\n", + "fmin_initial = 26.6679801306\n", + "fmin_final = 23.9480211593\n", + "========================\n", + "start F 24.3114060946, final F 23.9480211593\n", + "154.836525659\n", + "fmin_initial 26.6679801306\n", + "------------------------\n", + "LOGW -- Library L-BFGS/C\n", + "------------------------\n", + "time elapsed 0.0816729068756\n", + "========================\n", + "fmin_initial = 26.6679801306\n", + "fmin_final = 23.5424870946\n", + "========================\n", + "start F 23.9480211593, final F 23.5424870946\n", + "129.994222441\n", + "fmin_initial 26.6679801306\n", + "------------------------\n", + "LOGW -- Library L-BFGS/C\n", + "------------------------\n", + "time elapsed 0.0679929256439\n", + "========================\n", + "fmin_initial = 26.6679801306\n", + "fmin_final = 23.0938127101\n", + "========================\n", + "start F 23.5424870946, final F 23.0938127101\n", + "109.137671465\n", + "fmin_initial 26.6679801306\n", + "------------------------\n", + "LOGW -- Library L-BFGS/C\n", + "------------------------\n", + "time elapsed 0.0643360614777\n", + "========================\n", + "fmin_initial = 26.6679801306\n", + "fmin_final = 22.6018981032\n", + "========================\n", + "start F 23.0938127101, final F 22.6018981032\n", + "91.6273901189\n", + "fmin_initial 26.6679801306\n", + "------------------------\n", + "LOGW -- Library L-BFGS/C\n", + "------------------------\n", + "time elapsed 0.103790044785\n", + "========================\n", + "fmin_initial = 26.6679801306\n", + "fmin_final = 22.0678901471\n", + "========================\n", + "start F 22.6018981032, final F 22.0678901471\n", + "76.9264957488\n", + "fmin_initial 26.6679801306\n", + "------------------------\n", + "LOGW -- Library L-BFGS/C\n", + "------------------------\n", + "time elapsed 0.113461971283\n", + "========================\n", + "fmin_initial = 26.6679801306\n", + "fmin_final = 21.4938403744\n", + "========================\n", + "start F 22.0678901471, final F 21.4938403744\n", + "64.584244302\n", + "fmin_initial 26.6679801306\n", + "------------------------\n", + "LOGW -- Library L-BFGS/C\n", + "------------------------\n", + "time elapsed 0.147518157959\n", + "========================\n", + "fmin_initial = 26.6679801306\n", + "fmin_final = 20.8833029902\n", + "========================\n", + "start F 21.4938403744, final F 20.8833029902\n", + "54.222210065\n", + "fmin_initial 26.6679801306\n", + "------------------------\n", + "LOGW -- Library L-BFGS/C\n", + "------------------------\n", + "time elapsed 0.145044088364\n", + "========================\n", + "fmin_initial = 26.6679801306\n", + "fmin_final = 20.2407460491\n", + "========================\n", + "start F 20.8833029902, final F 20.2407460491\n", + "45.5226827551\n", + "fmin_initial 26.6679801306\n", + "------------------------\n", + "LOGW -- Library L-BFGS/C\n", + "------------------------\n", + "time elapsed 0.1254529953\n", + "========================\n", + "fmin_initial = 26.6679801306\n", + "fmin_final = 19.5715541061\n", + "========================\n", + "start F 20.2407460491, final F 19.5715541061\n", + "38.2189262063\n", + "fmin_initial 26.6679801306\n", + "------------------------\n", + "LOGW -- Library L-BFGS/C\n", + "------------------------\n", + "time elapsed 0.0899519920349\n", + "========================\n", + "fmin_initial = 26.6679801306\n", + "fmin_final = 18.8812513322\n", + "========================\n", + "start F 19.5715541061, final F 18.8812513322\n", + "32.0869999737\n", + "fmin_initial 26.6679801306\n", + "------------------------\n", + "LOGW -- Library L-BFGS/C\n", + "------------------------\n", + "time elapsed 0.107522010803\n", + "========================\n", + "fmin_initial = 26.6679801306\n", + "fmin_final = 18.1772164328\n", + "========================\n", + "start F 18.8812513322, final F 18.1772164328\n", + "26.9388930959\n", + "fmin_initial 26.6679801306\n", + "------------------------\n", + "LOGW -- Library L-BFGS/C\n", + "------------------------\n", + "time elapsed 0.128987073898\n", + "========================\n", + "fmin_initial = 26.6679801306\n", + "fmin_final = 17.4656643069\n", + "========================\n", + "start F 18.1772164328, final F 17.4656643069\n", + "22.6167594922\n", + "fmin_initial 26.6679801306\n", + "------------------------\n", + "LOGW -- Library L-BFGS/C\n", + "------------------------\n", + "time elapsed 0.185315132141\n", + "========================\n", + "fmin_initial = 26.6679801306\n", + "fmin_final = 16.7521179434\n", + "========================\n", + "start F 17.4656643069, final F 16.7521179434\n", + "18.9880782447\n", + "fmin_initial 26.6679801306\n", + "------------------------\n", + "LOGW -- Library L-BFGS/C\n", + "------------------------\n", + "time elapsed 0.169569015503\n", + "========================\n", + "fmin_initial = 26.6679801306\n", + "fmin_final = 16.0475254169\n", + "========================\n", + "start F 16.7521179434, final F 16.0475254169\n", + "15.9415903746\n", + "fmin_initial 26.6679801306\n", + "------------------------\n", + "LOGW -- Library L-BFGS/C\n", + "------------------------\n", + "time elapsed 0.148363113403\n", + "========================\n", + "fmin_initial = 26.6679801306\n", + "fmin_final = 15.347521127\n", + "========================\n", + "start F 16.0475254169, final F 15.347521127\n", + "13.3838875317\n", + "fmin_initial 26.6679801306\n", + "------------------------\n", + "LOGW -- Library L-BFGS/C\n", + "------------------------\n", + "time elapsed 0.15465593338\n", + "========================\n", + "fmin_initial = 26.6679801306\n", + "fmin_final = 14.662385741\n", + "========================\n", + "start F 15.347521127, final F 14.662385741\n", + "11.2365480014\n", + "fmin_initial 26.6679801306\n", + "------------------------\n", + "LOGW -- Library L-BFGS/C\n", + "------------------------\n", + "time elapsed 0.209790945053\n", + "========================\n", + "fmin_initial = 26.6679801306\n", + "fmin_final = 14.0100934414\n", + "========================\n", + "start F 14.662385741, final F 14.0100934414\n", + "9.4337322163\n", + "fmin_initial 26.6679801306\n", + "------------------------\n", + "LOGW -- Library L-BFGS/C\n", + "------------------------\n", + "time elapsed 0.140057086945\n", + "========================\n", + "fmin_initial = 26.6679801306\n", + "fmin_final = 13.3716349522\n", + "========================\n", + "start F 14.0100934414, final F 13.3716349522\n", + "7.92016405019\n", + "fmin_initial 26.6679801306\n", + "------------------------\n", + "LOGW -- Library L-BFGS/C\n", + "------------------------\n", + "time elapsed 0.184184074402\n", + "========================\n", + "fmin_initial = 26.6679801306\n", + "fmin_final = 12.7415976612\n", + "========================\n", + "start F 13.3716349522, final F 12.7415976612\n", + "6.64943599667\n", + "fmin_initial 26.6679801306\n", + "------------------------\n", + "LOGW -- Library L-BFGS/C\n", + "------------------------\n", + "time elapsed 0.21475481987\n", + "========================\n", + "fmin_initial = 26.6679801306\n", + "fmin_final = 12.1454879407\n", + "========================\n", + "start F 12.7415976612, final F 12.1454879407\n", + "5.58258626886\n", + "fmin_initial 26.6679801306\n", + "------------------------\n", + "LOGW -- Library L-BFGS/C\n", + "------------------------\n", + "time elapsed 0.16547703743\n", + "========================\n", + "fmin_initial = 26.6679801306\n", + "fmin_final = 11.5814561623\n", + "========================\n", + "start F 12.1454879407, final F 11.5814561623\n", + "4.68690419231\n", + "fmin_initial 26.6679801306\n", + "------------------------\n", + "LOGW -- Library L-BFGS/C\n", + "------------------------\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "time elapsed 0.21225810051\n", + "========================\n", + "fmin_initial = 26.6679801306\n", + "fmin_final = 11.0470957346\n", + "========================\n", + "start F 11.5814561623, final F 11.0470957346\n", + "3.9349272631\n", + "fmin_initial 26.6679801306\n", + "------------------------\n", + "LOGW -- Library L-BFGS/C\n", + "------------------------\n", + "time elapsed 0.401999950409\n", + "========================\n", + "fmin_initial = 26.6679801306\n", + "fmin_final = 10.5409248133\n", + "========================\n", + "start F 11.0470957346, final F 10.5409248133\n", + "3.30359912013\n", + "fmin_initial 26.6679801306\n", + "------------------------\n", + "LOGW -- Library L-BFGS/C\n", + "------------------------\n", + "time elapsed 0.402848958969\n", + "========================\n", + "fmin_initial = 26.6679801306\n", + "fmin_final = 10.0606534589\n", + "========================\n", + "start F 10.5409248133, final F 10.0606534589\n", + "2.7735626142\n", + "fmin_initial 26.6679801306\n", + "------------------------\n", + "LOGW -- Library L-BFGS/C\n", + "------------------------\n", + "time elapsed 0.320688962936\n", + "========================\n", + "fmin_initial = 26.6679801306\n", + "fmin_final = 9.62697456632\n", + "========================\n", + "start F 10.0606534589, final F 9.62697456632\n", + "2.3285662985\n", + "fmin_initial 26.6679801306\n", + "------------------------\n", + "LOGW -- Library L-BFGS/C\n", + "------------------------\n", + "time elapsed 0.401020050049\n", + "========================\n", + "fmin_initial = 26.6679801306\n", + "fmin_final = 9.20805402478\n", + "========================\n", + "start F 9.62697456632, final F 9.20805402478\n", + "1.95496614309\n", + "fmin_initial 26.6679801306\n", + "------------------------\n", + "LOGW -- Library L-BFGS/C\n", + "------------------------\n", + "time elapsed 0.459240913391\n", + "========================\n", + "fmin_initial = 26.6679801306\n", + "fmin_final = 8.8397799661\n", + "========================\n", + "start F 9.20805402478, final F 8.8397799661\n", + "1.64130719538\n", + "fmin_initial 26.6679801306\n", + "------------------------\n", + "LOGW -- Library L-BFGS/C\n", + "------------------------\n", + "time elapsed 0.644036054611\n", + "========================\n", + "fmin_initial = 26.6679801306\n", + "fmin_final = 8.49814646685\n", + "========================\n", + "start F 8.8397799661, final F 8.49814646685\n", + "1.37797235983\n", + "fmin_initial 26.6679801306\n", + "------------------------\n", + "LOGW -- Library L-BFGS/C\n", + "------------------------\n", + "time elapsed 0.668258190155\n", + "========================\n", + "fmin_initial = 26.6679801306\n", + "fmin_final = 8.18970018507\n", + "========================\n", + "start F 8.49814646685, final F 8.18970018507\n", + "1.15688752832\n", + "fmin_initial 26.6679801306\n", + "------------------------\n", + "LOGW -- Library L-BFGS/C\n", + "------------------------\n", + "time elapsed 0.872335910797\n", + "========================\n", + "fmin_initial = 26.6679801306\n", + "fmin_final = 7.91136943126\n", + "========================\n", + "start F 8.18970018507, final F 7.91136943126\n", + "0.971274019847\n", + "fmin_initial 26.6679801306\n", + "------------------------\n", + "LOGW -- Library L-BFGS/C\n", + "------------------------\n", + "time elapsed 0.709568023682\n", + "========================\n", + "fmin_initial = 26.6679801306\n", + "fmin_final = 7.6594589916\n", + "========================\n", + "start F 7.91136943126, final F 7.6594589916\n", + "0.815440739519\n", + "fmin_initial 26.6679801306\n", + "------------------------\n", + "LOGW -- Library L-BFGS/C\n", + "------------------------\n", + "time elapsed 1.24873614311\n", + "========================\n", + "fmin_initial = 26.6679801306\n", + "fmin_final = 7.42250677641\n", + "========================\n", + "start F 7.6594589916, final F 7.42250677641\n", + "0.684609683857\n", + "fmin_initial 26.6679801306\n", + "------------------------\n", + "LOGW -- Library L-BFGS/C\n", + "------------------------\n", + "time elapsed 1.49881005287\n", + "========================\n", + "fmin_initial = 26.6679801306\n", + "fmin_final = 7.21024040644\n", + "========================\n", + "start F 7.42250677641, final F 7.21024040644\n", + "0.574769442484\n", + "fmin_initial 26.6679801306\n", + "------------------------\n", + "LOGW -- Library L-BFGS/C\n", + "------------------------\n", + "time elapsed 1.26373100281\n", + "========================\n", + "fmin_initial = 26.6679801306\n", + "fmin_final = 7.02708532667\n", + "========================\n", + "start F 7.21024040644, final F 7.02708532667\n", + 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"fmin_final = 6.56520759034\n", + "========================\n", + "start F 6.71090070645, final F 6.56520759034\n", + "0.285559230199\n", + "fmin_initial 26.6679801306\n", + "------------------------\n", + "LOGW -- Library L-BFGS/C\n", + "------------------------\n", + "time elapsed 2.21602678299\n", + "========================\n", + "fmin_initial = 26.6679801306\n", + "fmin_final = 6.4650917071\n", + "========================\n", + "start F 6.56520759034, final F 6.4650917071\n", + "0.23974349678\n", + "fmin_initial 26.6679801306\n", + "------------------------\n", + "LOGW -- Library L-BFGS/C\n", + "------------------------\n", + "time elapsed 2.84477710724\n", + "========================\n", + "fmin_initial = 26.6679801306\n", + "fmin_final = 6.33453994437\n", + "========================\n", + "start F 6.4650917071, final F 6.33453994437\n", + "0.201278537585\n", + "fmin_initial 26.6679801306\n", + "------------------------\n", + "LOGW -- Library L-BFGS/C\n", + "------------------------\n", + "time elapsed 3.87801194191\n", + "========================\n", + "fmin_initial = 26.6679801306\n", + "fmin_final = 6.24204115864\n", + "========================\n", + "start F 6.33453994437, final F 6.24204115864\n", + "0.168984978681\n", + "fmin_initial 26.6679801306\n", + "------------------------\n", + "LOGW -- Library L-BFGS/C\n", + "------------------------\n", + "time elapsed 3.50900006294\n", + "========================\n", + "fmin_initial = 26.6679801306\n", + "fmin_final = 6.13415532846\n", + "========================\n", + "start F 6.24204115864, final F 6.13415532846\n", + "0.141872667412\n", + "fmin_initial 26.6679801306\n", + "------------------------\n", + "LOGW -- Library L-BFGS/C\n", + "------------------------\n", + "time elapsed 3.0575568676\n", + "========================\n", + "fmin_initial = 26.6679801306\n", + "fmin_final = 6.07784806769\n", + "========================\n", + "start F 6.13415532846, final F 6.07784806769\n", + 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L-BFGS/C\n", + "--------------------------\n", + "time elapsed 0.167870998383\n", + "========================\n", + "fmin_initial = 26.6576882565\n", + "fmin_final = 26.6574170124\n", + "========================\n", + "start F 26.6591106562, final F 26.6574170124\n", + "49682.3959473\n", + "fmin_initial 26.6557275292\n", + "--------------------------\n", + "FORCES -- Library L-BFGS/C\n", + "--------------------------\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "time elapsed 0.178367137909\n", + "========================\n", + "fmin_initial = 26.6557275292\n", + "fmin_final = 26.6554002003\n", + "========================\n", + "start F 26.6574170124, final F 26.6554002003\n", + "41711.2461206\n", + "fmin_initial 26.6533821656\n", + "--------------------------\n", + "FORCES -- Library L-BFGS/C\n", + "--------------------------\n", + "time elapsed 0.276947975159\n", + "========================\n", + "fmin_initial = 26.6533821656\n", + "fmin_final = 26.6529987009\n", + "========================\n", + "start F 26.6554002003, final F 26.6529987009\n", + "35019.0046143\n", + "fmin_initial 26.6505977075\n", + "--------------------------\n", + "FORCES -- Library L-BFGS/C\n", + "--------------------------\n", + "time elapsed 0.319915056229\n", + "========================\n", + "fmin_initial = 26.6505977075\n", + "fmin_final = 26.650139469\n", + "========================\n", + "start F 26.6529987009, final F 26.650139469\n", + "29400.4806433\n", + "fmin_initial 26.6472802577\n", + "--------------------------\n", + "FORCES -- Library L-BFGS/C\n", + "--------------------------\n", + "time elapsed 0.232867956161\n", + "========================\n", + "fmin_initial = 26.6472802577\n", + "fmin_final = 26.6467355414\n", + "========================\n", + "start F 26.650139469, final F 26.6467355414\n", + "24683.4046707\n", + "fmin_initial 26.6433328894\n", + "--------------------------\n", + "FORCES -- Library L-BFGS/C\n", + "--------------------------\n", + "time elapsed 0.287516832352\n", + "========================\n", + "fmin_initial = 26.6433328894\n", + "fmin_final = 26.642683422\n", + "========================\n", + "start F 26.6467355414, final F 26.642683422\n", + "20723.1464522\n", + "fmin_initial 26.6386294566\n", + "--------------------------\n", + "FORCES -- Library L-BFGS/C\n", + "--------------------------\n", + "time elapsed 0.22622013092\n", + "========================\n", + "fmin_initial = 26.6386294566\n", + "fmin_final = 26.6378597746\n", + "========================\n", + "start F 26.642683422, final F 26.6378597746\n", + "17398.2805293\n", + "fmin_initial 26.6330523534\n", + "--------------------------\n", + "FORCES -- Library L-BFGS/C\n", + "--------------------------\n", + "time elapsed 0.180597066879\n", + "========================\n", + "fmin_initial = 26.6330523534\n", + "fmin_final = 26.6321188679\n", + "========================\n", + "start F 26.6378597746, final F 26.6321188679\n", + "14606.8632036\n", + "fmin_initial 26.6263810403\n", + "--------------------------\n", + "FORCES -- Library L-BFGS/C\n", + "--------------------------\n", + "time elapsed 0.256871938705\n", + "========================\n", + "fmin_initial = 26.6263810403\n", + "fmin_final = 26.6252878788\n", + "========================\n", + "start F 26.6321188679, final F 26.6252878788\n", + "12263.3068418\n", + "fmin_initial 26.6184532795\n", + "--------------------------\n", + "FORCES -- Library L-BFGS/C\n", + "--------------------------\n", + "time elapsed 0.250482797623\n", + "========================\n", + "fmin_initial = 26.6184532795\n", + "fmin_final = 26.6171604086\n", + "========================\n", + "start F 26.6252878788, final F 26.6171604086\n", + "10295.7556731\n", + "fmin_initial 26.6090461706\n", + "--------------------------\n", + "FORCES -- Library L-BFGS/C\n", + "--------------------------\n", + "time elapsed 0.222979068756\n", + "========================\n", + "fmin_initial = 26.6090461706\n", + "fmin_final = 26.6074922883\n", + "========================\n", + "start F 26.6171604086, final F 26.6074922883\n", + "8643.8826206\n", + "fmin_initial 26.5978370497\n", + "--------------------------\n", + "FORCES -- Library L-BFGS/C\n", + "--------------------------\n", + "time elapsed 0.258507013321\n", + "========================\n", + "fmin_initial = 26.5978370497\n", + "fmin_final = 26.5959952162\n", + "========================\n", + "start F 26.6074922883, final F 26.5959952162\n", + "7257.03961232\n", + "fmin_initial 26.5844720688\n", + "--------------------------\n", + "FORCES -- Library L-BFGS/C\n", + "--------------------------\n", + "time elapsed 0.2008228302\n", + "========================\n", + "fmin_initial = 26.5844720688\n", + "fmin_final = 26.5823277533\n", + "========================\n", + "start F 26.5959952162, final F 26.5823277533\n", + "6092.70466137\n", + "fmin_initial 26.5686592687\n", + "--------------------------\n", + "FORCES -- Library L-BFGS/C\n", + "--------------------------\n", + "time elapsed 0.278023004532\n", + "========================\n", + "fmin_initial = 26.5686592687\n", + "fmin_final = 26.5660851935\n", + "========================\n", + "start F 26.5823277533, final F 26.5660851935\n", + "5115.17809929\n", + "fmin_initial 26.5498810034\n", + "--------------------------\n", + "FORCES -- Library L-BFGS/C\n", + "--------------------------\n", + "time elapsed 0.242169141769\n", + "========================\n", + "fmin_initial = 26.5498810034\n", + "fmin_final = 26.5467897799\n", + "========================\n", + "start F 26.5660851935, final F 26.5467897799\n", + "4294.48798879\n", + "fmin_initial 26.5275300928\n", + "--------------------------\n", + "FORCES -- Library L-BFGS/C\n", + "--------------------------\n", + "time elapsed 0.201272010803\n", + "========================\n", + "fmin_initial = 26.5275300928\n", + "fmin_final = 26.5238799254\n", + "========================\n", + "start F 26.5467897799, final F 26.5238799254\n", + "3605.47115425\n", + "fmin_initial 26.5009345951\n", + "--------------------------\n", + "FORCES -- Library L-BFGS/C\n", + "--------------------------\n", + "time elapsed 0.184623003006\n", + "========================\n", + "fmin_initial = 26.5009345951\n", + "fmin_final = 26.4966960162\n", + "========================\n", + "start F 26.5238799254, final F 26.4966960162\n", + "3027.00165376\n", + "fmin_initial 26.4695445912\n", + "--------------------------\n", + "FORCES -- Library L-BFGS/C\n", + "--------------------------\n", + "time elapsed 0.185946941376\n", + "========================\n", + "fmin_initial = 26.4695445912\n", + "fmin_final = 26.4644635956\n", + "========================\n", + "start F 26.4966960162, final F 26.4644635956\n", + "2541.3430367\n", + "fmin_initial 26.4323171898\n", + "--------------------------\n", + "FORCES -- Library L-BFGS/C\n", + "--------------------------\n", + "time elapsed 0.174301862717\n", + "========================\n", + "fmin_initial = 26.4323171898\n", + "fmin_final = 26.4262761994\n", + "========================\n", + "start F 26.4644635956, final F 26.4262761994\n", + "2133.6045265\n", + "fmin_initial 26.3881971788\n", + "--------------------------\n", + "FORCES -- Library L-BFGS/C\n", + "--------------------------\n", + "time elapsed 0.199242115021\n", + "========================\n", + "fmin_initial = 26.3881971788\n", + "fmin_final = 26.3810783047\n", + "========================\n", + "start F 26.4262761994, final F 26.3810783047\n", + "1791.28445462\n", + "fmin_initial 26.3360516547\n", + "--------------------------\n", + "FORCES -- Library L-BFGS/C\n", + "--------------------------\n", + "time elapsed 0.265172958374\n", + "========================\n", + "fmin_initial = 26.3360516547\n", + "fmin_final = 26.3276462812\n", + "========================\n", + "start F 26.3810783047, final F 26.3276462812\n", + "1503.88694696\n", + "fmin_initial 26.2744574057\n", + "--------------------------\n", + "FORCES -- Library L-BFGS/C\n", + "--------------------------\n", + "time elapsed 0.233819007874\n", + "========================\n", + "fmin_initial = 26.2744574057\n", + "fmin_final = 26.2645706112\n", + "========================\n", + "start F 26.3276462812, final F 26.2645706112\n", + "1262.60010987\n", + "fmin_initial 26.2018116294\n", + "--------------------------\n", + "FORCES -- Library L-BFGS/C\n", + "--------------------------\n", + "time elapsed 0.265710115433\n", + "========================\n", + "fmin_initial = 26.2018116294\n", + "fmin_final = 26.1902257163\n", + "========================\n", + "start F 26.2645706112, final F 26.1902257163\n", + "1060.02584881\n", + "fmin_initial 26.1163161776\n", + "--------------------------\n", + "FORCES -- Library L-BFGS/C\n", + "--------------------------\n", + "time elapsed 0.21554684639\n", + "========================\n", + "fmin_initial = 26.1163161776\n", + "fmin_final = 26.1027681158\n", + "========================\n", + "start F 26.1902257163, final F 26.1027681158\n", + "889.953035289\n", + "fmin_initial 26.0157688789\n", + "--------------------------\n", + "FORCES -- Library L-BFGS/C\n", + "--------------------------\n", + "time elapsed 0.20515203476\n", + "========================\n", + "fmin_initial = 26.0157688789\n", + "fmin_final = 26.0001173351\n", + "========================\n", + "start F 26.1027681158, final F 26.0001173351\n", + "747.167067587\n", + "fmin_initial 25.8981768541\n", + "--------------------------\n", + "FORCES -- Library L-BFGS/C\n", + "--------------------------\n", + "time elapsed 0.195495128632\n", + "========================\n", + "fmin_initial = 25.8981768541\n", + "fmin_final = 25.8799469457\n", + "========================\n", + "start F 26.0001173351, final F 25.8799469457\n", + "627.28998582\n", + "fmin_initial 25.760832699\n", + "--------------------------\n", + "FORCES -- Library L-BFGS/C\n", + "--------------------------\n", + "time elapsed 0.175618886948\n", + "========================\n", + "fmin_initial = 25.760832699\n", + "fmin_final = 25.7396855586\n", + "========================\n", + "start F 25.8799469457, final F 25.7396855586\n", + "526.646239348\n", + "fmin_initial 25.6009229807\n", + "--------------------------\n", + "FORCES -- Library L-BFGS/C\n", + "--------------------------\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "time elapsed 0.295466184616\n", + "========================\n", + "fmin_initial = 25.6009229807\n", + "fmin_final = 25.5765460665\n", + "========================\n", + "start F 25.7396855586, final F 25.5765460665\n", + "442.149990737\n", + "fmin_initial 25.4154507673\n", + "--------------------------\n", + "FORCES -- Library L-BFGS/C\n", + "--------------------------\n", + "time elapsed 0.284309864044\n", + "========================\n", + "fmin_initial = 25.4154507673\n", + "fmin_final = 25.3875565148\n", + "========================\n", + "start F 25.5765460665, final F 25.3875565148\n", + "371.210500907\n", + "fmin_initial 25.2012622389\n", + "--------------------------\n", + "FORCES -- Library L-BFGS/C\n", + "--------------------------\n", + "time elapsed 0.235403060913\n", + "========================\n", + "fmin_initial = 25.2012622389\n", + "fmin_final = 25.1696096938\n", + "========================\n", + "start F 25.3875565148, final F 25.1696096938\n", + "311.652694493\n", + "fmin_initial 24.9554358751\n", + "--------------------------\n", + "FORCES -- Library L-BFGS/C\n", + "--------------------------\n", + "time elapsed 0.299201011658\n", + "========================\n", + "fmin_initial = 24.9554358751\n", + "fmin_final = 24.9196088935\n", + "========================\n", + "start F 25.1696096938, final F 24.9196088935\n", + "261.650469875\n", + "fmin_initial 24.6745482385\n", + "--------------------------\n", + "FORCES -- Library L-BFGS/C\n", + "--------------------------\n", + "time elapsed 0.229416847229\n", + "========================\n", + "fmin_initial = 24.6745482385\n", + "fmin_final = 24.6344753679\n", + "========================\n", + "start F 24.9196088935, final F 24.6344753679\n", + "219.670709079\n", + "fmin_initial 24.3558710443\n", + "--------------------------\n", + "FORCES -- Library L-BFGS/C\n", + "--------------------------\n", + "time elapsed 0.202832937241\n", + "========================\n", + "fmin_initial = 24.3558710443\n", + "fmin_final = 24.3114163654\n", + "========================\n", + "start F 24.6344753679, final F 24.3114163654\n", + "184.426270859\n", + "fmin_initial 23.9969096375\n", + "--------------------------\n", + "FORCES -- Library L-BFGS/C\n", + "--------------------------\n", + "time elapsed 0.185986042023\n", + "========================\n", + "fmin_initial = 23.9969096375\n", + "fmin_final = 23.9480397456\n", + "========================\n", + "start F 24.3114163654, final F 23.9480397456\n", + "154.836525659\n", + "fmin_initial 23.5957912554\n", + "--------------------------\n", + "FORCES -- Library L-BFGS/C\n", + "--------------------------\n", + "time elapsed 0.315644979477\n", + "========================\n", + "fmin_initial = 23.5957912554\n", + "fmin_final = 23.5424962527\n", + "========================\n", + "start F 23.9480397456, final F 23.5424962527\n", + "129.994222441\n", + "fmin_initial 23.1506009056\n", + "--------------------------\n", + "FORCES -- Library L-BFGS/C\n", + "--------------------------\n", + "time elapsed 0.234272956848\n", + "========================\n", + "fmin_initial = 23.1506009056\n", + "fmin_final = 23.0938094435\n", + "========================\n", + "start F 23.5424962527, final F 23.0938094435\n", + "109.137671465\n", + "fmin_initial 22.6622516179\n", + "--------------------------\n", + "FORCES -- Library L-BFGS/C\n", + "--------------------------\n", + "time elapsed 0.248445034027\n", + "========================\n", + "fmin_initial = 22.6622516179\n", + "fmin_final = 22.6019113404\n", + "========================\n", + "start F 23.0938094435, final F 22.6019113404\n", + "91.6273901189\n", + "fmin_initial 22.1311128304\n", + "--------------------------\n", + "FORCES -- Library L-BFGS/C\n", + "--------------------------\n", + "time elapsed 0.258097887039\n", + "========================\n", + "fmin_initial = 22.1311128304\n", + "fmin_final = 22.0678426223\n", + "========================\n", + "start F 22.6019113404, final F 22.0678426223\n", + "76.9264957488\n", + "fmin_initial 21.5591209315\n", + "--------------------------\n", + "FORCES -- Library L-BFGS/C\n", + "--------------------------\n", + "time elapsed 0.301091909409\n", + "========================\n", + "fmin_initial = 21.5591209315\n", + "fmin_final = 21.4938242955\n", + "========================\n", + "start F 22.0678426223, final F 21.4938242955\n", + "64.584244302\n", + "fmin_initial 20.949717913\n", + "--------------------------\n", + "FORCES -- Library L-BFGS/C\n", + "--------------------------\n", + "time elapsed 0.285746097565\n", + "========================\n", + "fmin_initial = 20.949717913\n", + "fmin_final = 20.8832271237\n", + "========================\n", + "start F 21.4938242955, final F 20.8832271237\n", + "54.222210065\n", + "fmin_initial 20.307472843\n", + "--------------------------\n", + "FORCES -- Library L-BFGS/C\n", + "--------------------------\n", + "time elapsed 0.299205064774\n", + "========================\n", + "fmin_initial = 20.307472843\n", + "fmin_final = 20.2405226193\n", + "========================\n", + "start F 20.8832271237, final F 20.2405226193\n", + "45.5226827551\n", + "fmin_initial 19.6377581885\n", + "--------------------------\n", + "FORCES -- Library L-BFGS/C\n", + "--------------------------\n", + "time elapsed 0.318819046021\n", + "========================\n", + "fmin_initial = 19.6377581885\n", + "fmin_final = 19.5710810427\n", + "========================\n", + "start F 20.2405226193, final F 19.5710810427\n", + "38.2189262063\n", + "fmin_initial 18.9463750428\n", + "--------------------------\n", + "FORCES -- Library L-BFGS/C\n", + "--------------------------\n", + "time elapsed 0.309738874435\n", + "========================\n", + "fmin_initial = 18.9463750428\n", + "fmin_final = 18.8809524314\n", + "========================\n", + "start F 19.5710810427, final F 18.8809524314\n", + "32.0869999737\n", + "fmin_initial 18.2402611321\n", + "--------------------------\n", + "FORCES -- Library L-BFGS/C\n", + "--------------------------\n", + "time elapsed 0.331274986267\n", + "========================\n", + "fmin_initial = 18.2402611321\n", + "fmin_final = 18.1765925516\n", + "========================\n", + "start F 18.8809524314, final F 18.1765925516\n", + "26.9388930959\n", + "fmin_initial 17.5258200517\n", + "--------------------------\n", + "FORCES -- Library L-BFGS/C\n", + "--------------------------\n", + "time elapsed 0.359204053879\n", + "========================\n", + "fmin_initial = 17.5258200517\n", + "fmin_final = 17.4645491053\n", + "========================\n", + "start F 18.1765925516, final F 17.4645491053\n", + "22.6167594922\n", + "fmin_initial 16.8097283788\n", + "--------------------------\n", + "FORCES -- Library L-BFGS/C\n", + "--------------------------\n", + "time elapsed 0.35439491272\n", + "========================\n", + "fmin_initial = 16.8097283788\n", + "fmin_final = 16.751191896\n", + "========================\n", + "start F 17.4645491053, final F 16.751191896\n", + "18.9880782447\n", + "fmin_initial 16.0979511835\n", + "--------------------------\n", + "FORCES -- Library L-BFGS/C\n", + "--------------------------\n", + "time elapsed 0.385224103928\n", + "========================\n", + "fmin_initial = 16.0979511835\n", + "fmin_final = 16.0424725983\n", + "========================\n", + "start F 16.751191896, final F 16.0424725983\n", + "15.9415903746\n", + "fmin_initial 15.3961622034\n", + "--------------------------\n", + "FORCES -- Library L-BFGS/C\n", + "--------------------------\n", + "time elapsed 0.428978919983\n", + "========================\n", + "fmin_initial = 15.3961622034\n", + "fmin_final = 15.3437238634\n", + "========================\n", + "start F 16.0424725983, final F 15.3437238634\n", + "13.3838875317\n", + "fmin_initial 14.7086302883\n", + "--------------------------\n", + "FORCES -- Library L-BFGS/C\n", + "--------------------------\n", + "time elapsed 0.430984973907\n", + "========================\n", + "fmin_initial = 14.7086302883\n", + "fmin_final = 14.6595073856\n", + "========================\n", + "start F 15.3437238634, final F 14.6595073856\n", + "11.2365480014\n", + "fmin_initial 14.0395964623\n", + "--------------------------\n", + "FORCES -- Library L-BFGS/C\n", + "--------------------------\n", + "time elapsed 0.411180973053\n", + "========================\n", + "fmin_initial = 14.0395964623\n", + "fmin_final = 13.9935743359\n", + "========================\n", + "start F 14.6595073856, final F 13.9935743359\n", + "9.4337322163\n", + "fmin_initial 13.3921803099\n", + "--------------------------\n", + "FORCES -- Library L-BFGS/C\n", + "--------------------------\n", + "time elapsed 0.463189840317\n", + "========================\n", + "fmin_initial = 13.3921803099\n", + "fmin_final = 13.3488894261\n", + "========================\n", + "start F 13.9935743359, final F 13.3488894261\n", + "7.92016405019\n", + "fmin_initial 12.7676879568\n", + "--------------------------\n", + "FORCES -- Library L-BFGS/C\n", + "--------------------------\n", + "time elapsed 0.488970994949\n", + "========================\n", + "fmin_initial = 12.7676879568\n", + "fmin_final = 12.727757536\n", + "========================\n", + "start F 13.3488894261, final F 12.727757536\n", + "6.64943599667\n", + "fmin_initial 12.1694086103\n", + "--------------------------\n", + "FORCES -- Library L-BFGS/C\n", + "--------------------------\n", + "time elapsed 0.490879058838\n", + "========================\n", + "fmin_initial = 12.1694086103\n", + "fmin_final = 12.1320605996\n", + "========================\n", + "start F 12.727757536, final F 12.1320605996\n", + "5.58258626886\n", + "fmin_initial 11.5979232618\n", + "--------------------------\n", + "FORCES -- Library L-BFGS/C\n", + "--------------------------\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "time elapsed 0.558349132538\n", + "========================\n", + "fmin_initial = 11.5979232618\n", + "fmin_final = 11.5634491215\n", + "========================\n", + "start F 12.1320605996, final F 11.5634491215\n", + "4.68690419231\n", + "fmin_initial 11.0548383457\n", + "--------------------------\n", + "FORCES -- Library L-BFGS/C\n", + "--------------------------\n", + "time elapsed 0.568557024002\n", + "========================\n", + "fmin_initial = 11.0548383457\n", + "fmin_final = 11.0235607316\n", + "========================\n", + "start F 11.5634491215, final F 11.0235607316\n", + "3.9349272631\n", + "fmin_initial 10.5420161292\n", + "--------------------------\n", + "FORCES -- Library L-BFGS/C\n", + "--------------------------\n", + "time elapsed 0.638747930527\n", + "========================\n", + "fmin_initial = 10.5420161292\n", + "fmin_final = 10.5139579442\n", + "========================\n", + "start F 11.0235607316, final F 10.5139579442\n", + "3.30359912013\n", + "fmin_initial 10.0608985849\n", + "--------------------------\n", + "FORCES -- Library L-BFGS/C\n", + "--------------------------\n", + "time elapsed 0.711537122726\n", + "========================\n", + "fmin_initial = 10.0608985849\n", + "fmin_final = 10.0360214876\n", + "========================\n", + "start F 10.5139579442, final F 10.0360214876\n", + "2.7735626142\n", + "fmin_initial 9.61249726742\n", + "--------------------------\n", + "FORCES -- Library L-BFGS/C\n", + "--------------------------\n", + "time elapsed 0.851964950562\n", + "========================\n", + "fmin_initial = 9.61249726742\n", + "fmin_final = 9.59085534562\n", + "========================\n", + "start F 10.0360214876, final F 9.59085534562\n", + "2.3285662985\n", + "fmin_initial 9.19729751905\n", + "--------------------------\n", + "FORCES -- Library L-BFGS/C\n", + "--------------------------\n", + "time elapsed 0.869477033615\n", + "========================\n", + "fmin_initial = 9.19729751905\n", + "fmin_final = 9.17893946189\n", + "========================\n", + "start F 9.59085534562, final F 9.17893946189\n", + "1.95496614309\n", + "fmin_initial 8.81595881944\n", + "--------------------------\n", + "FORCES -- Library L-BFGS/C\n", + "--------------------------\n", + "time elapsed 0.996088981628\n", + "========================\n", + "fmin_initial = 8.81595881944\n", + "fmin_final = 8.80028709006\n", + "========================\n", + "start F 9.17893946189, final F 8.80028709006\n", + "1.64130719538\n", + "fmin_initial 8.46865537503\n", + "--------------------------\n", + "FORCES -- Library L-BFGS/C\n", + "--------------------------\n", + "time elapsed 0.792988061905\n", + "========================\n", + "fmin_initial = 8.46865537503\n", + "fmin_final = 8.45400122291\n", + "========================\n", + "start F 8.80028709006, final F 8.45400122291\n", + "1.37797235983\n", + "fmin_initial 8.15086135436\n", + "--------------------------\n", + "FORCES -- Library L-BFGS/C\n", + "--------------------------\n", + "time elapsed 0.916300058365\n", + "========================\n", + "fmin_initial = 8.15086135436\n", + "fmin_final = 8.13893413033\n", + "========================\n", + "start F 8.45400122291, final F 8.13893413033\n", + "1.15688752832\n", + "fmin_initial 7.86362403221\n", + "--------------------------\n", + "FORCES -- Library L-BFGS/C\n", + "--------------------------\n", + "time elapsed 1.00184893608\n", + "========================\n", + "fmin_initial = 7.86362403221\n", + "fmin_final = 7.85334024875\n", + "========================\n", + "start F 8.13893413033, final F 7.85334024875\n", + "0.971274019847\n", + "fmin_initial 7.60412866423\n", + "--------------------------\n", + "FORCES -- Library L-BFGS/C\n", + "--------------------------\n", + "time elapsed 1.3216509819\n", + "========================\n", + "fmin_initial = 7.60412866423\n", + "fmin_final = 7.59513572582\n", + "========================\n", + "start F 7.85334024875, final F 7.59513572582\n", + "0.815440739519\n", + "fmin_initial 7.37095997738\n", + "--------------------------\n", + "FORCES -- Library L-BFGS/C\n", + "--------------------------\n", + "time elapsed 1.77991390228\n", + "========================\n", + "fmin_initial = 7.37095997738\n", + "fmin_final = 7.36198933965\n", + "========================\n", + "start F 7.59513572582, final F 7.36198933965\n", + "0.684609683857\n", + "fmin_initial 7.15957061367\n", + "--------------------------\n", + "FORCES -- Library L-BFGS/C\n", + "--------------------------\n", + "time elapsed 1.95743203163\n", + "========================\n", + "fmin_initial = 7.15957061367\n", + "fmin_final = 7.15192179444\n", + "========================\n", + "start F 7.36198933965, final F 7.15192179444\n", + "0.574769442484\n", + "fmin_initial 6.9697667012\n", + "--------------------------\n", + "FORCES -- Library L-BFGS/C\n", + "--------------------------\n", + "time elapsed 2.17523312569\n", + "========================\n", + "fmin_initial = 6.9697667012\n", + "fmin_final = 6.96226859535\n", + "========================\n", + "start F 7.15192179444, final F 6.96226859535\n", + "0.482552204274\n", + "fmin_initial 6.79782028036\n", + "--------------------------\n", + "FORCES -- Library L-BFGS/C\n", + "--------------------------\n", + "time elapsed 2.22102499008\n", + "========================\n", + "fmin_initial = 6.79782028036\n", + "fmin_final = 6.79099848368\n", + "========================\n", + "start F 6.96226859535, final F 6.79099848368\n", + "0.405130496924\n", + "fmin_initial 6.64208724242\n", + "--------------------------\n", + "FORCES -- Library L-BFGS/C\n", + "--------------------------\n", + "time elapsed 2.40123414993\n", + "========================\n", + "fmin_initial = 6.64208724242\n", + "fmin_final = 6.63716484404\n", + "========================\n", + "start F 6.79099848368, final F 6.63716484404\n", + "0.340130493828\n", + "fmin_initial 6.50430726529\n", + "--------------------------\n", + "FORCES -- Library L-BFGS/C\n", + "--------------------------\n", + "time elapsed 2.60932898521\n", + "========================\n", + "fmin_initial = 6.50430726529\n", + "fmin_final = 6.49827599893\n", + "========================\n", + "start F 6.63716484404, final F 6.49827599893\n", + "0.285559230199\n", + "fmin_initial 6.37857169335\n", + "--------------------------\n", + "FORCES -- Library L-BFGS/C\n", + "--------------------------\n", + "time elapsed 2.38401794434\n", + "========================\n", + "fmin_initial = 6.37857169335\n", + "fmin_final = 6.37232459909\n", + "========================\n", + "start F 6.49827599893, final F 6.37232459909\n", + "0.23974349678\n", + "fmin_initial 6.26265625174\n", + "--------------------------\n", + "FORCES -- Library L-BFGS/C\n", + "--------------------------\n", + "time elapsed 2.28793597221\n", + "========================\n", + "fmin_initial = 6.26265625174\n", + "fmin_final = 6.25863725477\n", + "========================\n", + "start F 6.37232459909, final F 6.25863725477\n", + "0.201278537585\n", + "fmin_initial 6.16004193789\n", + "--------------------------\n", + "FORCES -- Library L-BFGS/C\n", + "--------------------------\n", + "time elapsed 2.9059817791\n", + "========================\n", + "fmin_initial = 6.16004193789\n", + "fmin_final = 6.15628442233\n", + "========================\n", + "start F 6.25863725477, final F 6.15628442233\n", + "0.168984978681\n", + "fmin_initial 6.06692624977\n", + "--------------------------\n", + "FORCES -- Library L-BFGS/C\n", + "--------------------------\n", + "time elapsed 0.890651941299\n", + "========================\n", + "fmin_initial = 6.06692624977\n", + "fmin_final = 6.06558607333\n", + "========================\n", + "start F 6.15628442233, final F 6.06558607333\n", + "0.141872667412\n", + "fmin_initial 5.98827065697\n", + "--------------------------\n", + "FORCES -- Library L-BFGS/C\n", + "--------------------------\n", + "time elapsed 3.92588591576\n", + "========================\n", + "fmin_initial = 5.98827065697\n", + "fmin_final = 5.9805067965\n", + "========================\n", + "start F 6.06558607333, final F 5.9805067965\n", + "0.119110313328\n", + "fmin_initial 5.90817000204\n", + "--------------------------\n", + "FORCES -- Library L-BFGS/C\n", + "--------------------------\n", + "time elapsed 1.10130906105\n", + "========================\n", + "fmin_initial = 5.90817000204\n", + "fmin_final = 5.90713139828\n", + "========================\n", + "start F 5.9805067965, final F 5.90713139828\n", + "0.1\n", + "fmin_initial 5.84477807166\n", + "--------------------------\n", + "FORCES -- Library L-BFGS/C\n", + "--------------------------\n", + "time elapsed 4.25780892372\n", + "========================\n", + "fmin_initial = 5.84477807166\n", + "fmin_final = 5.83981284217\n", + "========================\n", + "start F 5.90713139828, final F 5.83981284217\n" + ] + } + ], + "source": [ + "recalc = True\n", + "\n", + "output_d = defaultdict(dict)\n", + "\n", + "for method in ['log-weights', 'forces']:\n", + " \n", + " output_d[method] = defaultdict(dict)\n", + "\n", + "\n", + "for method in ['log-weights', 'forces']:\n", + "\n", + " for set_i, karplus_set in enumerate(set_names_l):\n", + " print(karplus_set)\n", + " \n", + " out_path = \"out/\"+method+\"/\"+karplus_set\n", + " \n", + " if os.path.exists(out_path+\"/opt_data.dat\") and not recalc:\n", + " print(\"already calculated\")\n", + " \n", + " # need to load previous calculations\n", + " \n", + " else:\n", + " # Start timer\n", + " start_th_series = time.time()\n", + " \n", + " opt_pth=karplus_set+\"_for_opt/\"\n", + " print opt_pth\n", + " \n", + " #initial weights\n", + " w0 = np.genfromtxt(opt_pth+\"/w0.txt\")\n", + " # copy to output directory\n", + " ! mkdir -p $out_path\"/for_opt\"\n", + " #! echo $out_path+\"for_opt\"\n", + " print(out_path+\"/for_opt\")\n", + " np.savetxt(out_path+\"/for_opt/w0.txt\", w0)\n", + "\n", + " \n", + " #N = 50001\n", + " w0= np.matrix(w0).T\n", + " \n", + " # Observables for each structure scaled by the inverse of the experimental error\n", + " yTilde = np.genfromtxt(opt_pth+\"/yTilde.txt\")\n", + " \n", + " # Experimental observables divided by the experimental uncertainty\n", + " Y_Tilde = np.genfromtxt(opt_pth+\"/YTilde.txt\")\n", + " Y_Tilde = Y_Tilde.reshape(1, Y_Tilde.shape[0])\n", + " \n", + " # Experimental observables\n", + " y = np.genfromtxt(opt_pth+\"/y.txt\")\n", + " \n", + " # BioEn optimization\n", + " wopt_d, yopt_d, L_d, chi2_d, S_d = run_theta_series(\n", + " w0, y, yTilde, Y_Tilde,\n", + " theta_l=theta_ar, cfg=cfg, verbose=True,\n", + " opt_method=method)\n", + " \n", + " # End timer\n", + " end_th_series = time.time()\n", + " dt_th_series = end_th_series - start_th_series\n", + " \n", + " # Outputs\n", + " \n", + " # copy inital observables to output dir\n", + " np.savetxt(out_path+\"/for_opt/y.txt\", y)\n", + " \n", + " output_d[method][karplus_set] = [wopt_d, yopt_d, L_d, chi2_d, S_d, dt_th_series]\n", + " \n", + " ! mkdir -p out/\n", + " ! mkdir -p \"out/\"$method\n", + " \n", + " ! mkdir -p $out_path\n", + " ! mkdir -p $out_path\"/wopt\"; mkdir -p $out_path\"/yopt\"\n", + " \n", + " \n", + " np.savetxt(out_path+\"/time.txt\", np.vstack((start_th_series,\n", + " end_th_series,\n", + " dt_th_series)))\n", + " \n", + " overview_table(\n", + " theta_ar, L_d, chi2_d, S_d, out_path=out_path) \n", + " bioen_save_out_array(\n", + " wopt_d, output_name=out_path+\"/wopt/w_theta{}_dat.txt\")\n", + " bioen_save_out_array(\n", + " yopt_d, output_name=out_path+\"/yopt/y_theta{}_dat.txt\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Analysis\n", + "\n", + "Focus on results of calculation with generalized forces.\n", + "\n", + "#### Setup" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": {}, + "outputs": [], + "source": [ + "def optimal_theta_entropy_change(theta_l, s_ar, deltaS=0.5, verbose=False):\n", + " \"\"\"\n", + " Find theta that gives desired entropy change\n", + " \"\"\" \n", + " s_index = np.argmin(np.absolute(s_ar - deltaS))\n", + " theta = theta_l[s_index]\n", + " if verbose:\n", + " print(s_index, theta_l[s_index], s_ar[s_index])\n", + " return theta, s_index\n", + "\n", + "\n", + "def format_ala5_chi_s_plot(fig, ax, xlabel=r\"$\\mathregular{\\chi^2}$\", ylabel=r\"$\\mathregular{S_{KL}}$\",\n", + " xlim=[-0.1, 3.8]):\n", + " ax.set_xlim(xlim[0], xlim[1])\n", + " ax.set_ylabel(xlabel)\n", + " ax.set_xlabel(ylabel)\n", + " ax.set_xticks([0, 1,2,3])\n", + " ax.set_xticks([0.5, 1.5, 2.5], minor=True)\n", + " fig.tight_layout()\n", + " return fig, ax" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "for_opt opt_data.dat time.txt wopt yopt\r\n" + ] + } + ], + "source": [ + "! ls out/forces/dft2" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "metadata": {}, + "outputs": [], + "source": [ + "para_set = \"dft2\"\n", + "pth_out = \"out/forces/\"+para_set" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "metadata": {}, + "outputs": [], + "source": [ + "opt_out = np.genfromtxt(pth_out+\"/opt_data.dat\")" + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "metadata": {}, + "outputs": [], + "source": [ + "theta_ar = opt_out[:,0]" + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "55" + ] + }, + "execution_count": 31, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "np.argmin(np.absolute(opt_out[:,1] - 0.5))" + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(55, 6.6494359966650451, 0.50066782960344114)\n" + ] + } + ], + "source": [ + "th_all, th_all_index = optimal_theta_entropy_change(\n", + "theta_ar, opt_out[:,1], deltaS=0.5, verbose=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "metadata": {}, + "outputs": [], + "source": [ + "w_s05 = np.genfromtxt(pth_out+\"/wopt/w_theta{}_dat.txt\".format(th_all))" + ] + }, + { + "cell_type": "code", + "execution_count": 34, + "metadata": {}, + "outputs": [], + "source": [ + "w0 = np.genfromtxt(pth_out+\"/for_opt/w0.txt\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Elbow plots" + ] + }, + { + "cell_type": "code", + "execution_count": 35, + "metadata": {}, + "outputs": [], + "source": [ + "cl = sns.color_palette()" + ] + }, + { + "cell_type": "code", + "execution_count": 36, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig, ax = plt.subplots(figsize=(3,3))\n", + "sns.set_style('ticks')\n", + "ax.plot(opt_out[:,1], opt_out[:,2], \"o\", ms=4)\n", + "ax.plot(opt_out[:,1][th_all_index], opt_out[:,2][th_all_index], \"s\", mfc=\"None\",\n", + " mec=\"black\", mew=1,label=r\"$\\mathregular{\\theta}=$\"+\"{:.2f}\".format(\n", + "th_all))\n", + "fig, ax = format_ala5_chi_s_plot(fig, ax, xlim=[-0.1,3.8])\n", + "fig.tight_layout()\n", + "! mkdir -p plots/opt_overview\n", + "fig.savefig(\"plots/opt_overview/ala5_L-curve_{}.pdf\".format(para_set))" + ] + }, + { + "cell_type": "code", + "execution_count": 37, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0.314389353965\n" + ] + } + ], + "source": [ + "print opt_out[:,2][th_all_index] / 28" + ] + }, + { + "cell_type": "code", + "execution_count": 38, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig, ax = plt.subplots(1,2, figsize=(6.5,3))\n", + "sns.set_style('ticks')\n", + "ax[0].plot(opt_out[:,1], opt_out[:,2], \"o\", ms=4)\n", + "ax[0].plot(opt_out[:,1][th_all_index], opt_out[:,2][th_all_index], \"s\", mfc=\"None\",\n", + "mec=\"black\", mew=1,label=r\"$\\mathregular{S_{KL}=0.5}$\")\n", + "fig, ax[0] = format_ala5_chi_s_plot(fig, ax[0], xlim=[-0.1,3.8])\n", + "ax[1].plot(np.cumsum(np.sort(w0)[::-1]), c=cl[2], label=\"reference\")\n", + "ax[1].plot(np.cumsum(np.sort(w_s05)[::-1]), c=cl[0], label=r\"$\\mathregular{S_{KL}=0.5}$\")\n", + "ax[1].set_ylim(0,1)\n", + "ax[1].set_xlabel(\"Weight index\", fontsize=14)\n", + "ax[1].set_ylabel(\"Cumulative weights\", fontsize=14)\n", + "ax[0].text(-0.15,0.99, \"A\", fontsize=19, transform=ax[0].transAxes)\n", + "ax[1].text(-0.3,0.99, \"B\", fontsize=19, transform=ax[1].transAxes)\n", + "\n", + "for ai, a in enumerate(['A', 'B']):\n", + " ax[ai].legend(fontsize=13, handletextpad=0.2)\n", + " #ax[ai].text(-0.3,1.01, a , fontsize=18, transform=ax[ai].transAxes)\n", + " ax[ai].tick_params(axis='both', which='major', labelsize=13)\n", + " fig.tight_layout()\n", + "\n", + "! mkdir -p plots/opt_overview\n", + "fig.savefig(\"plots/opt_overview/ala_chi2_S_{}.pdf\".format(para_set))" + ] + }, + { + "cell_type": "code", + "execution_count": 39, + "metadata": {}, + "outputs": [], + "source": [ + "# save for replotting L-curves\n", + "np.savetxt(\"plots/opt_overview/s_chi2_{:.2f}\".format(th_all),\n", + "np.column_stack((opt_out[:,1], opt_out[:,2])))" + ] + }, + { + "cell_type": "code", + "execution_count": 40, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([ 6.649436 , 0.50066783, 8.80290191, 12.1320606 ])" + ] + }, + "execution_count": 40, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "opt_out[th_all_index]" + ] + }, + { + "cell_type": "code", + "execution_count": 41, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "6.64943599667\n" + ] + } + ], + "source": [ + "print th_all" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Ramachandran plots" + ] + }, + { + "cell_type": "code", + "execution_count": 42, + "metadata": {}, + "outputs": [], + "source": [ + "import matplotlib.colors as colors\n", + "\n", + "def phi_psi_pmf(phi_psi, nbins=100, return_edges=False, weights=None, missing_counts_g=15):\n", + " c, ex, ey = phi_psi_hist(phi_psi, nbins=nbins, weights=weights)\n", + " lnp = ln_p_hist_counts(c, missing_counts_g=missing_counts_g)\n", + " \n", + " if return_edges:\n", + " return lnp, ex, ey\n", + " else:\n", + " return lnp\n", + " \n", + "\n", + "def ln_p_hist_counts(c, missing_counts_g=15):\n", + " ln_counts = -np.log(c / float(c.sum()))\n", + " ln_counts[np.isinf(ln_counts)] = missing_counts_g\n", + " return ln_counts\n", + "\n", + "def phi_psi_hist(phi_psi, nbins=100, weights=None):\n", + " c, xe, ye = np.histogram2d(phi_psi[:,0], phi_psi[:,1], bins=nbins,\n", + " range=[[-180,180],[-180,180]],\n", + " weights=weights)\n", + " return c, xe, ye \n", + "\n", + "\n", + "def plot_rama_delta_g(free_energy_2d, fig_ax=None, vmin=-3, vmax=3, label=\"MD-PDB\", cmap='bwr'):\n", + " \n", + " if np.all(fig_ax) is None:\n", + " fig, ax = plt.subplots(figsize=(4.5,4.5))\n", + " else:\n", + " fig, ax = fig_ax\n", + " \n", + " im = ax.imshow(free_energy_2d, origin=\"low\",\n", + " extent=[-180,180, -180,180], label=label, cmap=cmap, vmin=vmin, vmax=vmax)\n", + "\n", + " cb = plt.colorbar(im, ax=ax, fraction=0.046, pad=0.04, norm=colors.Normalize(vmin=vmin, vmax=vmax))\n", + " \n", + " ax.set_xticks(np.arange(-180, 180+90, step=90));\n", + " ax.set_yticks(np.arange(-180, 180+90, step=90));\n", + " \n", + " ax.set_xlabel(\"$\\mathregular{\\phi \\; [^{\\circ}]}$\", fontsize=13)\n", + " ax.set_ylabel(\"$\\mathregular{\\psi \\; [^{\\circ}]}$\", fontsize=13)\n", + " \n", + " return fig, ax, cb" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "\n", + "##### MD results" + ] + }, + { + "cell_type": "code", + "execution_count": 43, + "metadata": {}, + "outputs": [], + "source": [ + "#p=\"/home/tb/lustelzl/DATA/Projects/BioEn/5ala/ala5_ph2_2/run_1us_2/jcoupling_sk10/\"\n", + "# /home/tb/lustelzl/DATA/Projects/BioEn/5ala/ala5_ph2_2/run_1us_2/jcoupling_sk10/phi_psi_deg.tar.gz" + ] + }, + { + "cell_type": "code", + "execution_count": 44, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "c_phi_psi_deg_2 c_phi_psi_deg_4 c_phi_psi_deg_6\r\n", + "c_phi_psi_deg_3 c_phi_psi_deg_5\r\n" + ] + } + ], + "source": [ + "! ls c_phi_psi*" + ] + }, + { + "cell_type": "code", + "execution_count": 45, + "metadata": {}, + "outputs": [], + "source": [ + "p=\".\"\n", + "fn = p+\"/c_phi_psi_deg_{}\"\n", + "step = 2\n", + "res_phi_psi_ar_l = [np.genfromtxt(fn.format(i))[::step] for i in range(3,6)]" + ] + }, + { + "cell_type": "code", + "execution_count": 46, + "metadata": {}, + "outputs": [], + "source": [ + "md_phi_psi = np.vstack(res_phi_psi_ar_l)" + ] + }, + { + "cell_type": "code", + "execution_count": 47, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(150003, 2)" + ] + }, + "execution_count": 47, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "md_phi_psi.shape" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "##### Optimal BioEn ensemble " + ] + }, + { + "cell_type": "code", + "execution_count": 48, + "metadata": {}, + "outputs": [], + "source": [ + "md_w_all = np.hstack(([w_s05]*3))" + ] + }, + { + "cell_type": "code", + "execution_count": 49, + "metadata": {}, + "outputs": [], + "source": [ + "nbins = 65\n", + "missing_pmf = 16" + ] + }, + { + "cell_type": "code", + "execution_count": 50, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/tb/lustelzl/anaconda2/lib/python2.7/site-packages/ipykernel_launcher.py:14: RuntimeWarning: divide by zero encountered in log\n", + " \n" + ] + } + ], + "source": [ + "lnp_all = phi_psi_pmf(md_phi_psi, weights=md_w_all,\n", + " nbins=nbins, missing_counts_g=missing_pmf)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 51, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/tb/lustelzl/anaconda2/lib/python2.7/site-packages/ipykernel_launcher.py:14: RuntimeWarning: divide by zero encountered in log\n", + " \n" + ] + } + ], + "source": [ + "lnp_md = phi_psi_pmf(md_phi_psi, nbins=nbins, return_edges=False, missing_counts_g=missing_pmf)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### PDB statistics" + ] + }, + { + "cell_type": "code", + "execution_count": 52, + "metadata": {}, + "outputs": [], + "source": [ + "#! cp /home/tb/lustelzl/DATA/Projects/BioEn/5ala/coil-ala/ala_phi_psi.txt ." + ] + }, + { + "cell_type": "code", + "execution_count": 53, + "metadata": {}, + "outputs": [], + "source": [ + "pdb_phi_psi = np.genfromtxt(\"ala_phi_psi.txt\")" + ] + }, + { + "cell_type": "code", + "execution_count": 54, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/tb/lustelzl/anaconda2/lib/python2.7/site-packages/ipykernel_launcher.py:14: RuntimeWarning: divide by zero encountered in log\n", + " \n" + ] + } + ], + "source": [ + "lnp_pdb = phi_psi_pmf(pdb_phi_psi, nbins=nbins, return_edges=False, missing_counts_g=missing_pmf)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Plot" + ] + }, + { + "cell_type": "code", + "execution_count": 55, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "/home/tb/lustelzl/DATA/Projects/BioEn/release-bioen/bioen-public/BioEn/examples/ala5_optimize\r\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "! pwd\n", + "! mkdir -p plots\n", + "! mkdir -p plots/phi_psi_lnp\n", + "\n", + "fig, ax = plt.subplots(2,2, figsize=(7.,6))\n", + "\n", + "fig, ax[0,0], cb0 = plot_rama_delta_g(lnp_md.T, vmin=1, vmax=16, fig_ax=[fig, ax[0,0]], cmap=\"terrain\")\n", + "fig, ax[0,1], cb1 = plot_rama_delta_g(lnp_pdb.T, vmin=1, vmax=16, fig_ax=[fig, ax[0,1]], cmap=\"terrain\")\n", + "\n", + "fig, ax[1,0], cb2 = plot_rama_delta_g(lnp_all.T, vmin=1, vmax=16, fig_ax=[fig, ax[1,0]], cmap='terrain')\n", + "fig, ax[1,1], cb3 = plot_rama_delta_g(lnp_all.T - lnp_md.T, vmin=-3, vmax=3, fig_ax=[fig, ax[1,1]])\n", + "\n", + "cb3.set_ticks([-3, -2, -1, 0, 1, 2, 3])\n", + "cb3.set_ticklabels(['-3', '-2', '-1', '0', '1', '2', '3'])\n", + "\n", + "for cb in [cb0, cb1, cb2]:\n", + " cb.set_ticks([2,4, 6,8,10,12,14, 16])\n", + " cb.set_ticklabels(['2', '4', '6', '8', '10', '12', '14', '$\\mathregular{\\geq 16}$'])\n", + "\n", + "cb0.set_label(r\"$\\mathregular{- \\ln p_{MD} \\; \\left[k_BT \\right]}$\",\n", + " fontsize=13)\n", + "cb1.set_label(r\"$\\mathregular{- \\ln p_{PDB} \\; \\left[k_BT \\right]}$\",\n", + " fontsize=13)\n", + "\n", + "cb2.set_label(r\"$\\mathregular{- \\ln p_{BioEn} \\; \\left[k_BT \\right]}$\",\n", + " fontsize=13)\n", + "cb3.set_label(r\"$\\mathregular{\\Delta G_{BioEn-MD} \\; \\left[k_BT \\right]}$\",\n", + " fontsize=13)\n", + "\n", + "\n", + "for ai, a in enumerate(['A', 'B', 'C', 'D']):\n", + " ax.flat[ai].text(-320, 160, a, fontsize=18)\n", + "\n", + "\n", + "fig.tight_layout()\n", + "fig.savefig(\"plots/phi_psi_lnp/{}_lnp_phi_psi_all.pdf\".format(para_set))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 2", + "language": "python", + "name": "python2" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 2 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython2", + "version": "2.7.12" + }, + "latex_envs": { + "LaTeX_envs_menu_present": true, + "autoclose": false, + "autocomplete": true, + "bibliofile": "biblio.bib", + "cite_by": "apalike", + "current_citInitial": 1, + "eqLabelWithNumbers": true, + "eqNumInitial": 1, + "hotkeys": { + "equation": "Ctrl-E", + "itemize": "Ctrl-I" + }, + "labels_anchors": false, + "latex_user_defs": false, + "report_style_numbering": true, + "user_envs_cfg": false + }, + "toc": { + "nav_menu": {}, + "number_sections": true, + "sideBar": true, + "skip_h1_title": false, + "title_cell": "Table of Contents", + "title_sidebar": "Contents", + "toc_cell": true, + "toc_position": {}, + "toc_section_display": true, + "toc_window_display": true + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/examples/ala5_optimize/ala5-release-1.tar.gz b/examples/ala5_optimize/ala5-release-1.tar.gz new file mode 100644 index 0000000000000000000000000000000000000000..ff08b55b9ed39806fc595d5a402a634a1b19fffe GIT binary patch literal 34618146 zcmV(tKy-5j5Rho$O5_&HJQbQ6D z5$QIhNLP9Zy^}~skq!wxB1j7mkQ7Wva)0-G_~v{0?%V(2u9tOY&zUoO?b+w-Su@Ae z`-S4Y7j9wq++!8#{)ZqhFR!Yse1lG2O;Jr%(Ek?^;s2@sFO|Um7G8m|f&YK|Ubz3C&yoLsaO3~w z|4Sg6c8M-PE6|CKVT;n!ENjvsyxl)yb>m@$yTyVm#(ms!dRqF%(6>)Qb~mZ;$_-Mu{7-;lqyG~&A6aa0QObZ!>Qi}<+J=$OBe*qsFg7yz_PNtnr(Wo6XLNRTJMnX&q9>1L0 zJfq>LsJ8m?uKH7oqeOrvVH0p(`3IVfrqVV6G$@ryKr`Bu1yB zRu;3?0HxWt(>kfL#MpJ>Oy-w0@)~UxkEMm$Q#Sz^U=I?bg{q|eYmx&KNfH?7Ds}Sk zfVM=|#KL+JzrDsq3!oO7ITZDd&83wV>{Qb>2LS;wEI@@jVPf#m7OnC7kneDuM|-n$XFWO zW91yR0zrdTNVFZuUknl9u{1S0Ww)|Gt862N2SF)cT*&Q*J4=y(a7{R17vKsk-c--R z?;^bW(4z|oV_H0UZfXFu2c0d(lB=mp)a98^<@nK6QQ9)%J9W!q8U4>msFV@al>*8{PE!9YQ*f}Q_7*VoxeNuUMbTXST-jMLes+jjApCJa zQZHaP;RG^?`Uvm_1p*VOPK5UM$^|t)2!?XDc^ch7rW{S7XK0boBX9<;qsf`rl7WUF z9XgS@FrvSm$f9wu4nPBb4?2x@g}P7-yp;X%tNN3Ve-ndQUc_19rXmtK0$=Lxw_&DZ_tqoc`;kBlM;ddCr+Yg%$UR zpuz~FGlX`a4-GZ`5AqBCn#8*F2_8-EMrXDJVeGPY7LaW;6orHAcmRwMgofc5p*mn; 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